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Abstract

This paper describes a weighted finite-state transducer composition algorithm that
generalizes the notion of the composition filter and present filters that remove
useless epsilon paths and push forward labels and weights along epsilon paths. This
filtering allows us to compose together large speech recognition context-dependent
lexicons and language models much more efficiently in time and space than
previously possible. We present experiments on Broadcast News and Google Search by
Voice that demonstrate a 5% to 10% overhead for dynamic, runtime composition
compared to a static, offline composition of the recognition transducer. To our
knowledge, this is the first such system with such small overhead.